An Athena query fails after running for several minutes, and the team needs stage-level timing and data volumes to find the bottleneck. What should they do first?
Show answer and explanation
Answer: D
EXPLAIN ANALYZE runs the query and returns its distributed execution plan with per-stage timings, row counts, and data processed. That shows where time and data are spent, which is the information needed to diagnose a slow or failing query. Raising timeouts or limits lets the same problem run longer or cost more without fixing it, and S3 access logs record requests rather than query plans.
Why the other options are wrong
- A. A longer timeout only delays the failure. It leaves the bottleneck unchanged and does not show which stage is responsible.
- B. S3 server access logs record individual object requests. They do not expose the query plan or stage timings the team needs.
- C. The scanned-data limit is a cost guardrail that stops queries over a threshold. Raising it hides the cost problem and does not diagnose the cause.